Huawei H13-321_V2.0 Exam : HCIP-AI-EI Developer V2.0

  • Exam Code: H13-321_V2.0
  • Exam Name: HCIP-AI-EI Developer V2.0
  • Updated: Aug 18, 2026
  • Q & A: 574 Questions and Answers

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Huawei H13-321_V2.0 Exam Syllabus Topics:

SectionWeightObjectives
Overview of ModelArts4%- ModelArts platform architecture
- Development and deployment workflow
Overview of Huawei AI Development Strategy and Full-Stack, All-Scenario AI Portfolio2%
Theoretical Knowledge and Applications of Speech Processing10%- Speech recognition and synthesis
- Speech processing basics
- Acoustic and language models
Speech Processing Lab Guide12%- Speech application development on ModelArts
ModelArts Platform Development Experiment10%
Natural Language Processing Lab Guide10%- NLP project practice on ModelArts
Image Processing Lab Guide12%- ModelArts-based image development
Theoretical Knowledge and Applications of Natural Language Processing10%- Text processing and representation
- Common NLP tasks and applications
- NLP core concepts
Theoretical Knowledge and Applications of Image Processing26%- Computer vision overview
- Image preprocessing and feature extraction
- Convolutional neural networks
- Image recognition, detection and segmentation
Neural Network Basics4%- Artificial neural network principles
- Network training and optimization
- Deep learning fundamentals

Huawei HCIP-AI-EI Developer V2.0 Sample Questions:

1. After the ModelArts automatic learning object detection model is deployed, users can add pictures for testing. Which of the following options are output prediction results? (Multiple choice)

A) Label predicted by each detection box
B) Confidence of each detection box
C) Four-point coordinates of each detection box
D) The true label of each detection box


2. The facial features detection API detects facial features of the input image and returns the coordinates of key points representing the location of the facial features. How many landmark points are currently supported?

A) 20
B) 68
C) 50
D) 10


3. Which one affects the number of weights in 2D convolution? (Multiple choice)

A) Number of convolution kernels
B) Width
C) Depth
D) High


4. In image preprocessing, the function of () is to remove noise, etc.


5. Which of the following belongs to the optimizer in gradient descent? (Multiple choice)

A) momentum
B) Adam (improved version of the optimizer)
C) Adaboost (Ensemble learning idea)
D) Adagrad (Adaptive Gradient Descent Optimizer)


Solutions:

Question # 1
Answer: A,B,C
Question # 2
Answer: B
Question # 3
Answer: A,B,D
Question # 4
Answer: Only visible for members
Question # 5
Answer: A,B,D

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